Gendering risk at what cost: Negotiations of gender and risk in Canadian women’s prisons
Bibliographic record
Abstract
A major turning point in Canadian federal1 women’s corrections occurred nearly 14 years ago when the federal government accepted the report of The Taskforce on Federally Sentenced Women (TTFSW, 1990). This report mandated a new women-centered and culturally sensitive approach to the management and organization of federal women’s prisons. Yet many of the difficulties identified by the taskforce still exist (Hannah-Moffat and Shaw, 2001) and are subject to ongoing evaluation by state and non-state agencies, including the Auditor General (Auditor General of Canada, 2003) and the Canadian Human Rights Commission.2 A major concern for Canadian researchers is the potential for systemic discrimination resulting from gender-neutral risk assessment practices that ineffectually account for gender and cultural differences (see also Van Voorhis and Presser, 2001, for the USA context). Risk tools have an intuitive appeal to practitioners because they ground deci-sions in statistical (thus objective) relationships (Feeley and Simon, 1992). Strategically, such tools are used to inform service rationalization and to increase professionals ’ accountability in decision making in the named efficient and just management of a range of risks (recidivism, suicide, self-harm, violence, escape). Whilst risk assessments may be considered by some practitioners (institutional classification officers, parole board members, probation officers and case managers) as a ‘matter of common sense’, such a discourse is not persuasive in court or at inquests, so a standardized risk assessment ensures a decision is defensible should something go wrong: ‘they back you up if something goes wrong – you can demonstrate that you used a standardized approach that is empirically based’.3
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.067 | 0.028 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".